P0276
Improving Selection for Feed Efficiency and Methane Emissions in Cattle Using Integrative ‘-Omics’ Technologies and Systems Biology Approach

Stephanie Lam , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
Filippo Miglior , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
Leluo Guan , Livestock Gentec, Department of Agricultural, Food & Nutritional Sciences, University of Alberta, Edmonton, AB, Canada
Sinead Waters , Animal and Grassland Research and Innovation Centre, Carlow, Ireland
Stephen P. Miller , Angus Genetics Inc., Saint Joseph, MO
Victoria Asselstine , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
Luiz F. Brito , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
Dave Seymour , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
Paul Stothard , Livestock Gentec, Department of Agricultural, Food & Nutritional Sciences, University of Alberta, Edmonton, AB, Canada
Graham S Plastow , Livestock Gentec, Department of Agricultural, Food & Nutritional Sciences, University of Alberta, Edmonton, AB, Canada
Angela Cánovas , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
Improving selection efficiency for economically and environmentally important traits, including feed efficiency (FE) and methane emissions (ME) can improve beef and dairy production. This study aims to evaluate host metabolic efficiency using high-throughput ‘–OMICS’ technology (i.e. metagenomics, metatranscriptomics, transcriptomics, metabolomics) to characterize the microbiome, and its association with key regulator genes and biomarkers, metabolic pathways, and casual mutations related to FE traits, such as residual feed intake (RFI; kg/d), and ME (g/d). Rumen fluid, target-tissue (i.e. liver, muscle, fat), and blood samples will be collected from the 40 most divergent animals with HIGH- and LOW-RFI in dairy and beef cattle populations. Metagenomics will be performed using 16S and 18S rRNA gene amplification to identify and quantify microbial functional capacity. For in depth microbiome evaluation, metatranscriptomics will be used to identify and quantify the microbial phylotypes. Transcriptomics on rumen fluid and target-tissues will be performed using RNA-Sequencing to study whole transcriptome gene expression and structural variation identification (i.e. SNP, deletions, splice variants). Additionally, metabolomics (gas-chromatography, mass-spectrometry and NMR analysis) will be used to identify circulating plasma metabolites and biomarkers associated with FE and ME. Structural and functional genomic data will be integrated into a systems biology approach to identify specific microbiome profiles, key regulator genes, metabolic pathways, and casual mutations associated with FE and ME. The final goal is to incorporate the functional variants into genomic predictions to develop more robust approaches for genetic selection in industry breeding plans to improve FE and reduce ME in dairy and beef cattle.
Back to: Genes & Pathways (Gene Expression, Proteomics, Metabolomics): Functional Analysis
<< Previous Poster | Next Poster >>